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A self-driven ESN-DSS approach for effective COVID-19 time series prediction and modelling – CORRIGENDUM

Published online by Cambridge University Press:  04 January 2026

Weiye Wang ,
Qing Li  and
Junsong Wang Open the ORCID record for Junsong Wang [Opens in a new window]
Weiye Wang
Affiliation:
School of Automation, Beijing Information Science and Technology University, Beijing, China Ministry of Education Key Laboratory of Modern Measurement and Control Technology, Beijing, China
Qing Li
Affiliation:
School of Automation, Beijing Information Science and Technology University, Beijing, China Ministry of Education Key Laboratory of Modern Measurement and Control Technology, Beijing, China
Junsong Wang*
Affiliation:
College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China
*
Corresponding author: Junsong Wang; Email: junsongwang.phd@gmail.com
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Abstract

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Information

Type
Corrigendum
Information
Epidemiology & Infection , Volume 153 , 2025 , e140
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Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0), which permits non-commercial re-use, distribution, and reproduction in any medium, provided that no alterations are made and the original article is properly cited. The written permission of Cambridge University Press must be obtained prior to any commercial use and/or adaptation of the article.
Copyright
© The Author(s), 2026. Published by Cambridge University Press

When this article was originally published in Epidemiology & Infection it contained some incorrect figures. The correct versions of figures 9 and 11 can be seen below:

Figure 9.

The change of MAPE with increasing the number of reservoir neurons $ N $ for confirmed cases forecasting for nine countries.

Figure 11.

The variation of MAPE with increasing spectral radius $ \rho (W) $ for confirmed cases forecasting for nine countries.

The authors apologise for this error.

References

Wang, W, Li, Q, Wang, J. (2024). A self-driven ESN-DSS approach for effective COVID-19 time series prediction and modelling. Epidemiology and Infection. 152, e146. https://doi.org/10.1017/S0950268824000992CrossRefGoogle ScholarPubMed
Figure 0

Figure 9. The change of MAPE with increasing the number of reservoir neurons $ N $ for confirmed cases forecasting for nine countries.

Figure 1

Figure 11. The variation of MAPE with increasing spectral radius $ \rho (W) $ for confirmed cases forecasting for nine countries.